Incident Library
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  1. Symptoms

    At 10:01 UTC on 2024-03-13 a Kubernetes node entered NotReady state, with CPU requests at 78% and memory requests at 82%. An alert fired in Slack at 10:03 and was picked up at 10:05. An attempt to SSH into the node at 10:13 failed because it was unresponsive; it started responding again at 10:19.

    Trigger

    Pods of a dev container that used Hangfire to trigger jobs failed with OOM and crash-looped on a node where a Grafana Agent pod was already using a lot of memory.

    Root cause
    Component
    Contributing factor
    Detection

    A Slack alert fired two minutes after the node went NotReady and was picked up two minutes later; the second node's NotReady at 15:03 also raised a Slack alert.

    Remediation
    • marked the EC2 instance unhealthy so a new node started
    • cordoned and force-drained the NotReady node and terminated its EC2 instance
    • removed the new LimitRange from the Grafana Agent namespace and restarted the Grafana Agent pod
    • scaled the dev container to 0 replicas, after which EC2 network traffic returned to normal
    • after the second NotReady, scaled the dev container to 0 replicas again and added a new node
    • emailed the team that owns the dev container, which confirmed it was working on a fix
    Action items
    • Move grafana-agent to the monitoring node group
    • Add alerts for when bandwidth across the cluster reaches a certain threshold
    • Investigate whether memory limits can be set on the grafana-agent Helm chart
    • Investigate grafana-agent memory usage
    • Ask Grafana support whether grafana-agent should use a PVC
    • Consider pushing node logs to Loki with limited retention
    • Create a wiki page with common metrics and log queries to check when investigating issues with Kubernetes nodes
    Lessons

    Dev workloads mixed with production let one crash-looping dev container take down nodes that production also used, and a monitoring agent outside its dedicated node group added to the memory pressure. Scaling the suspect container back up before its owners had fixed it brought the failure back on the replacement node. The team also noted that action items from earlier post-mortems need follow-up, and that knowledge of useful CloudWatch metrics had not been shared, which made the diagnosis harder.

Check your cluster for this

This case involves Application database and Container memory limit. kubegap audits a Kubernetes cluster for gaps like these, read-only, from Claude Code or Codex; its checks cite public reports like the ones in this library.

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